# Sparse Portfolio Selection via Quasi-Norm Regularization

arXiv: https://arxiv.org/abs/1312.6350 · HTML: https://mlsanity.com/paper/1312.6350/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Dec 2013.

## Built on by (most cited first, 2 of 2)

- [Decision-focused Sparse Tangent Portfolio Optimization](https://mlsanity.com/paper/2607.00581/) (2026)
- [Constrained Optimization Involving Nonconvex ℓp Norms: Optimality Conditions, Algorithm and Convergence](https://mlsanity.com/paper/2110.14127/) (2021)

## Abstract (arXiv)

In this paper, we propose ℓp-norm regularized models to seek near-optimal sparse portfolios. These sparse solutions reduce the complexity of portfolio implementation and management. Theoretical results are established to guarantee the sparsity of the second-order KKT points of the ℓp-norm regularized models. More interestingly, we present a theory that relates sparsity of the KKT points with Projected correlation and Projected Sharpe ratio. We also design an interior point algorithm to obtain an approximate second-order KKT solution of the ℓp-norm models in polynomial time with a fixed error tolerance, and then test our ℓp-norm modes on S&P 500 (2008-2012) data and international market data. The computational results illustrate that the ℓp-norm regularized models can generate portfolios of any desired sparsity with portfolio variance and portfolio return comparable to those of the unregularized Markowitz model with cardinality constraint. Our analysis of a combined model lead us to conclude that sparsity is not directly related to overfitting at all. Instead, we find that sparsity moderates overfitting only indirectly. A combined ℓ1-ℓp model shows that the proper choose of leverage, which is the amount of additional buying-power generated by selling short can mitigate overfitting; A combined ℓ2-ℓp model is able to produce extremely high performing portfolios that exceeded the 1/N strategy and all ℓ1 and ℓ2 regularized portfolios.